Deep learning is powerful but opaque. Recent advances have shown that such models are powerful enough to achieve or exceed human level performance in classification of images [1]} and games such as Go [2]} and DOTA [3]}. As a parametric representation for function, deep neural networks also see broad applications in solving differential equation [4]}, [5]}. Recent advances have also that these models are opaque in ways that prevent application in high-stakes domains [6]}, and leave them vulnerable to adversaries. Moreover, applying models to problem domains remains a challenge because we lack theoretical motivation or understanding of the consequences of different choices. An important direction for the field is to develop tools by which we may obtain a clearer understanding of the power and limits of deep learning.
